Strategic Implications of Openness in AI Development
| Author | Nick Bostrom |
| Date | 01 May 2017 |
| Published date | 01 May 2017 |
| DOI | http://doi.org/10.1111/1758-5899.12403 |
Strategic Implications of Openness in AI
Development
Nick Bostrom
Future of Humanity Institute, University of Oxford, Oxford, United Kingdom
Abstract
This paper attempts a preliminary analysis of the global desirability of different forms of openness in AI development (includ-
ing openness about source code, science, data, safety techniques, capabilities, and goals). Short-term impacts of increased
openness appear mostly socially beneficial in expectation. The strategic implications of medium and long-term impacts are
complex. The evaluation of long-term impacts, in particular, may depend on whether the objective is to benefit the present
generation or to promote a time-neutral aggregate of well-being of future generations. Some forms of openness are plausibly
positive on both counts (openness about safety measures, openness about goals). Others (openness about source code,
science, and possibly capability) could lead to a tightening of the competitive situation around the time of the introduction of
advanced AI, increasing the probability that winning the AI race is incompatible with using any safety method that incurs a
delay or limits performance. We identify several key factors that must be taken into account by any well-founded opinion on
the matter.
Policy Implications
•The global desirability of openness in AI development –sharing e.g. source code, algorithms, or scientific insights –
depends –on complex tradeoffs.
•A central concern is that openness could exacerbate a racing dynamic: competitors trying to be the first to develop
advanced (superintelligent) AI may accept higher levels of existential risk in order to accelerate progress.
•Openness may reduce the probability of AI benefits being monopolized by a small group, but other potential political con-
sequences are more problematic.
•Partial openness that enables outsiders to contribute to an AI project’s safety work and to supervise organizational plans
and goals appears desirable.
The goal of this paper is to conduct a preliminary analysis
of the long-term strategic implications of openness in AI
development. What effects would increased openness in AI
development have, on the margin, on the long-term impacts
of AI? Is the expected value for society of these effects posi-
tive or negative? Since it is typically impossible to provide
definitive answers to this type of question, our ambition
here is more modest: to introduce some relevant considera-
tions and develop some thoughts on their weight and plau-
sibility. Given recent interest in the topic of openness in AI
and the absence (to our knowledge) of any academic work
directly addressing this issue, even this modest ambition
would offer scope for a worthwhile contribution.
Openness in AI development can refer to various things.
For example, we could use this phrase to refer to open
source code, open science, open data, or to openness
about safety techniques, capabilities, and organizational
goals, or to a non-proprietary development regime gener-
ally. We will have something to say about each of those
different aspects of openness –they do not all have the
same strategic implications. But unless we specify
otherwise, we will use the shorthand ‘openness’to refer to
the practice of releasing into the public domain (continu-
ously and as promptly as is practicable) all relevant source
code and platforms and publishing freely about algorithms
and scientific insights and ideas gained in the course of
the research.
Currently, most leading AI developers operate with a high
but not maximal degree of openness. AI researchers at Goo-
gle, Facebook, Microsoft and Baidu regularly present their
latest work at technical conferences and post it on preprint
servers. So do researchers in academia. Sometimes, but not
always, these publications are accompanied by a release of
source code, which makes it easier for outside researchers
to replicate the work and build on it. Each of the aforemen-
tioned companies have developed and released under open
source licences source code for platforms that help
researchers (and students and other interested folk) imple-
ment machine learning architectures. The movement of staff
and interns is another important vector for the spread of
ideas. The recently announced OpenAI initiative even has
openness explicitly built into its brand identity.
Global Policy (2019) 8:2 doi: 10.1111/1758-5899.12403©2017 The Authors Global Policy published by Durham University and John Wiley & Sons, Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited.
Global Policy Volume 8 . Issue 2 . May 2017135
Research Article
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